Analysis of agricultural components of the resource potential of EAEU countries using economico-mathematical methods

The study is driven by the need to enhance the efficiency of agro-industrial production and adapt the EAEU countries to changing economic conditions. Economic and mathematical methods, including hierarchical clustering, principal component analysis (PCA), and machine learning, are used to analyze the resource potential of the agro-industrial complex of the EAEU countries to determine trajectories for improving efficiency. These approaches have revealed structural relationships between agricultural and macroeconomic parameters. The aim of the study is to identify the factors influencing the differentiation of agricultural development in the EAEU countries, focusing on increasing the strategic returns on their resource potential. To achieve this, an analysis of agricultural indicators, such as irrigated land area, grain crop yield, fertilizer consumption, and water use in agriculture, was conducted, as well as their relationships with economic indicators, including the share of agriculture in GDP, agricultural product exports, and imports. The PCA method allowed for dimensionality reduction, identifying factors that explain 65% of the variance. Based on PCA and cluster analysis, the EAEU countries were grouped into categories: high agricultural potential (Russia, Kazakhstan), medium agricultural development level (Belarus), and limited agricultural resources (Armenia, Kyrgyzstan). Econometric modeling showed that the key factors influencing agricultural efficiency are irrigated land area, average annual precipitation, and fertilizer consumption. These indicators significantly impact grain crop yields and the macroeconomic parameters of the agro-industrial complex. Analysis using machine learning methods revealed the high significance of water use in agriculture and agricultural product exports for countries with high agricultural potential. The results obtained can be applied to optimize resource use in the EAEU countries and develop economic policy strategies aimed at enhancing the competitiveness of the agricultural sector under unfavorable macroeconomic dynamics.

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Publication Details

Journal
Mezhdunarodnyi sel skokhozyaistvennyi zhurnal
Published
2025-08-15
DOI
https://doi.org/10.55186/25876740_2025_68_4_538
Primary Topic
Water and Wastewater Treatment
Type
article
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article

Analysis of agricultural components of the resource potential of EAEU countries using economico-mathematical methods

Nikolay Dmitriev, Fatima Aguzarova, Dmitry Rodionov
Mezhdunarodnyi sel skokhozyaistvennyi zhurnal
Water and Wastewater Treatment
article

Analysis of agricultural components of the resource potential of EAEU countries using economico-mathematical methods

Nikolay Dmitriev, Fatima Aguzarova, Dmitry Rodionov
article en

Abstract

The study is driven by the need to enhance the efficiency of agro-industrial production and adapt the EAEU countries to changing economic conditions. Economic and mathematical methods, including hierarchical clustering, principal component analysis (PCA), and machine learning, are used to analyze the resource potential of the agro-industrial complex of the EAEU countries to determine trajectories for improving efficiency. These approaches have revealed structural relationships between agricultural and macroeconomic parameters. The aim of the study is to identify the factors influencing the differentiation of agricultural development in the EAEU countries, focusing on increasing the strategic returns on their resource potential. To achieve this, an analysis of agricultural indicators, such as irrigated land area, grain crop yield, fertilizer consumption, and water use in agriculture, was conducted, as well as their relationships with economic indicators, including the share of agriculture in GDP, agricultural product exports, and imports. The PCA method allowed for dimensionality reduction, identifying factors that explain 65% of the variance. Based on PCA and cluster analysis, the EAEU countries were grouped into categories: high agricultural potential (Russia, Kazakhstan), medium agricultural development level (Belarus), and limited agricultural resources (Armenia, Kyrgyzstan). Econometric modeling showed that the key factors influencing agricultural efficiency are irrigated land area, average annual precipitation, and fertilizer consumption. These indicators significantly impact grain crop yields and the macroeconomic parameters of the agro-industrial complex. Analysis using machine learning methods revealed the high significance of water use in agriculture and agricultural product exports for countries with high agricultural potential. The results obtained can be applied to optimize resource use in the EAEU countries and develop economic policy strategies aimed at enhancing the competitiveness of the agricultural sector under unfavorable macroeconomic dynamics.

Mezhdunarodnyi sel skokhozyaistvennyi zhurnal
Peter the Great St. Petersburg Polytechnic University (RU), North Ossetian State University (RU)
Openalex Percentile: Top 36%
Water and Wastewater Treatment
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